datahub/docs/lineage/airflow.md

# Airflow Integration

:::note

If you're looking to schedule DataHub ingestion using Airflow, see the guide on [scheduling ingestion with Airflow](../../metadata-ingestion/schedule_docs/airflow.md).

:::

The DataHub Airflow plugin supports:

- Automatic column-level lineage extraction from various operators e.g. SQL operators (including `MySqlOperator`, `PostgresOperator`, `SnowflakeOperator`, `BigQueryInsertJobOperator`, and more), `S3FileTransformOperator`, and more.
- Airflow DAG and tasks, including properties, ownership, and tags.
- Task run information, including task successes and failures.
- Manual lineage annotations using `inlets` and `outlets` on Airflow operators.

There's two implementations of the plugin, with different Airflow version support.

| Approach  | Airflow Versions | Notes                                                                                   |
| --------- | ---------------- | --------------------------------------------------------------------------------------- |
| Plugin v2 | 2.3.4+           | Recommended. Requires Python 3.8+                                                       |
| Plugin v1 | 2.3 - 2.8        | Deprecated. No automatic lineage extraction; may not extract lineage if the task fails. |

If you're using Airflow older than 2.3, it's possible to use the v1 plugin with older versions of `acryl-datahub-airflow-plugin`. See the [compatibility section](#compatibility) for more details.

<!-- TODO: Update the local Airflow guide and link to it here. -->
<!-- If you are looking to run Airflow and DataHub using docker locally, follow the guide [here](../../docker/airflow/local_airflow.md). -->

## DataHub Plugin v2

### Installation

The v2 plugin requires Airflow 2.3+ and Python 3.8+. If you don't meet these requirements, see the [compatibility section](#compatibility) for other options.

```shell
pip install 'acryl-datahub-airflow-plugin[plugin-v2]'
```

### Configuration

Set up a DataHub connection in Airflow, either via command line or the Airflow UI.

#### Command Line

```shell
airflow connections add  --conn-type 'datahub-rest' 'datahub_rest_default' --conn-host 'http://datahub-gms:8080' --conn-password '<optional datahub auth token>'
```

If you are using DataHub Cloud then please use `https://YOUR_PREFIX.acryl.io/gms` as the `--conn-host` parameter.

#### Airflow UI

On the Airflow UI, go to Admin -> Connections and click the "+" symbol to create a new connection. Select "DataHub REST Server" from the dropdown for "Connection Type" and enter the appropriate values.

<p align="center">
  <img width="70%"  src="https://raw.githubusercontent.com/datahub-project/static-assets/main/imgs/airflow/plugin_connection_setup.png"/>
</p>

#### Optional Configurations

No additional configuration is required to use the plugin. However, there are some optional configuration parameters that can be set in the `airflow.cfg` file.

```ini title="airflow.cfg"
[datahub]
# Optional - additional config here.
enabled = True  # default
```

| Name                       | Default value        | Description                                                                              |
| -------------------------- | -------------------- | ---------------------------------------------------------------------------------------- |
| enabled                    | true                 | If the plugin should be enabled.                                                         |
| conn_id                    | datahub_rest_default | The name of the datahub rest connection.                                                 |
| cluster                    | prod                 | name of the airflow cluster, this is equivalent to the `env` of the instance             |
| capture_ownership_info     | true                 | Extract DAG ownership.                                                                   |
| capture_ownership_as_group | false                | When extracting DAG ownership, treat DAG owner as a group rather than a user             |
| capture_tags_info          | true                 | Extract DAG tags.                                                                        |
| capture_executions         | true                 | Extract task runs and success/failure statuses. This will show up in DataHub "Runs" tab. |
| materialize_iolets         | true                 | Create or un-soft-delete all entities referenced in lineage.                             |
| enable_extractors          | true                 | Enable automatic lineage extraction.                                                     |
| disable_openlineage_plugin | true                 | Disable the OpenLineage plugin to avoid duplicative processing.                          |
| log_level                  | _no change_          | [debug] Set the log level for the plugin.                                                |
| debug_emitter              | false                | [debug] If true, the plugin will log the emitted events.                                 |

## DataHub Plugin v1

### Installation

The v1 plugin requires Airflow 2.3 - 2.8 and Python 3.8+. If you're on older versions, it's still possible to use an older version of the plugin. See the [compatibility section](#compatibility) for more details.

Note that the v1 plugin is less featureful than the v2 plugin, and is overall not actively maintained.
Since datahub v0.15.0, the v2 plugin has been the default. If you need to use the v1 plugin with `acryl-datahub-airflow-plugin` v0.15.0+, you must also set the environment variable `DATAHUB_AIRFLOW_PLUGIN_USE_V1_PLUGIN=true`.

```shell
pip install 'acryl-datahub-airflow-plugin[plugin-v1]'

# The DataHub rest connection type is included by default.
# To use the DataHub Kafka connection type, install the plugin with the kafka extras.
pip install 'acryl-datahub-airflow-plugin[plugin-v1,datahub-kafka]'
```

<!-- This plugin registers a task success/failure callback on every task with a cluster policy and emits DataHub events from that. This allows this plugin to be able to register both task success as well as failures compared to the older Airflow Lineage Backend which could only support emitting task success. -->

### Configuration

#### Disable lazy plugin loading

```ini title="airflow.cfg"
[core]
lazy_load_plugins = False
```

On MWAA you should add this config to your [Apache Airflow configuration options](https://docs.aws.amazon.com/mwaa/latest/userguide/configuring-env-variables.html#configuring-2.0-airflow-override).

#### Setup a DataHub connection

You must configure an Airflow connection for Datahub. We support both a Datahub REST and a Kafka-based connections, but you only need one.

```shell
# For REST-based:
airflow connections add  --conn-type 'datahub_rest' 'datahub_rest_default' --conn-host 'http://datahub-gms:8080' --conn-password '<optional datahub auth token>'
# For Kafka-based (standard Kafka sink config can be passed via extras):
airflow connections add  --conn-type 'datahub_kafka' 'datahub_kafka_default' --conn-host 'broker:9092' --conn-extra '{}'
```

#### Configure the plugin

If your config doesn't align with the default values, you can configure the plugin in your `airflow.cfg` file.

```ini title="airflow.cfg"
[datahub]
enabled = true
conn_id = datahub_rest_default  # or datahub_kafka_default
# etc.
```

| Name                       | Default value        | Description                                                                                                                                                                            |
| -------------------------- | -------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| enabled                    | true                 | If the plugin should be enabled.                                                                                                                                                       |
| conn_id                    | datahub_rest_default | The name of the datahub connection you set in step 1.                                                                                                                                  |
| cluster                    | prod                 | name of the airflow cluster                                                                                                                                                            |
| capture_ownership_info     | true                 | If true, the owners field of the DAG will be capture as a DataHub corpuser.                                                                                                            |
| capture_ownership_as_group | false                | When extracting DAG ownership, treat DAG owner as a group rather than a user.                                                                                                          |
| capture_tags_info          | true                 | If true, the tags field of the DAG will be captured as DataHub tags.                                                                                                                   |
| capture_executions         | true                 | If true, we'll capture task runs in DataHub in addition to DAG definitions.                                                                                                            |
| materialize_iolets         | true                 | Create or un-soft-delete all entities referenced in lineage.                                                                                                                           |
| render_templates           | true                 | If true, jinja-templated fields will be automatically rendered to improve the accuracy of SQL statement extraction.                                                                    |
| datajob_url_link           | taskinstance         | If taskinstance, the datajob url will be taskinstance link on airflow. It can also be grid.                                                                                            |
|                            |
| graceful_exceptions        | true                 | If set to true, most runtime errors in the lineage backend will be suppressed and will not cause the overall task to fail. Note that configuration issues will still throw exceptions. |
| dag_filter_str             | { "allow": [".*"] }  | AllowDenyPattern value in form of JSON string to filter the DAGs from running.                                                                                                         |

#### Validate that the plugin is working

1. Go and check in Airflow at Admin -> Plugins menu if you can see the DataHub plugin
2. Run an Airflow DAG. In the task logs, you should see Datahub related log messages like:

```
Emitting DataHub ...
```

## Automatic lineage extraction

Only the v2 plugin supports automatic lineage extraction. If you're using the v1 plugin, you must use manual lineage annotation or emit lineage directly.

To automatically extract lineage information, the v2 plugin builds on top of Airflow's built-in [OpenLineage extractors](https://openlineage.io/docs/integrations/airflow/default-extractors).
As such, we support a superset of the default operators that Airflow/OpenLineage supports.

The SQL-related extractors have been updated to use [DataHub's SQL lineage parser](https://blog.datahubproject.io/extracting-column-level-lineage-from-sql-779b8ce17567), which is more robust than the built-in one and uses DataHub's metadata information to generate column-level lineage.

Supported operators:

- `SQLExecuteQueryOperator`, including any subclasses. Note that in newer versions of Airflow (generally Airflow 2.5+), most SQL operators inherit from this class.
- `AthenaOperator` and `AWSAthenaOperator`
- `BigQueryOperator` and `BigQueryExecuteQueryOperator`
- `BigQueryInsertJobOperator` (incubating)
- `MySqlOperator`
- `PostgresOperator`
- `RedshiftSQLOperator`
- `SnowflakeOperator` and `SnowflakeOperatorAsync`
- `SqliteOperator`
- `TrinoOperator`

<!--
These operators are supported by OpenLineage, but we haven't tested them yet:
- `SQLCheckOperator`, `SQLValueCheckOperator`, `SQLThresholdCheckOperator`, `SQLIntervalCheckOperator`, `SQLColumnCheckOperator`, `BigQueryColumnCheckOperator`
- `FTPFileTransmitOperator`
- `GCSToGCSOperator`
- `S3CopyObjectOperator`
- `S3FileTransformOperator`
- `SageMakerProcessingOperator` and `SageMakerProcessingOperatorAsync`
- `SFTPOperator`

There's also a few operators (e.g. BashOperator, PythonOperator) that have custom extractors, but those extractors don't generate lineage.
-->

Known limitations:

- We do not fully support operators that run multiple SQL statements at once. In these cases, we'll only capture lineage from the first SQL statement.

## Manual Lineage Annotation

### Using `inlets` and `outlets`

You can manually annotate lineage by setting `inlets` and `outlets` on your Airflow operators. This is useful if you're using an operator that doesn't support automatic lineage extraction, or if you want to override the automatic lineage extraction.

We have a few code samples that demonstrate how to use `inlets` and `outlets`:

- [`lineage_backend_demo.py`](../../metadata-ingestion-modules/airflow-plugin/src/datahub_airflow_plugin/example_dags/lineage_backend_demo.py)
- [`lineage_backend_taskflow_demo.py`](../../metadata-ingestion-modules/airflow-plugin/src/datahub_airflow_plugin/example_dags/lineage_backend_taskflow_demo.py) - uses the [TaskFlow API](https://airflow.apache.org/docs/apache-airflow/stable/concepts/taskflow.html)

For more information, take a look at the [Airflow lineage docs](https://airflow.apache.org/docs/apache-airflow/stable/lineage.html).

### Custom Operators

If you have created a [custom Airflow operator](https://airflow.apache.org/docs/apache-airflow/stable/howto/custom-operator.html) that inherits from the BaseOperator class,
when overriding the `execute` function, set inlets and outlets via `context['ti'].task.inlets` and `context['ti'].task.outlets`.
The DataHub Airflow plugin will then pick up those inlets and outlets after the task runs.

```python
class DbtOperator(BaseOperator):
    ...

    def execute(self, context):
        # do something
        inlets, outlets = self._get_lineage()
        # inlets/outlets are lists of either datahub_airflow_plugin.entities.Dataset or datahub_airflow_plugin.entities.Urn
        context['ti'].task.inlets = self.inlets
        context['ti'].task.outlets = self.outlets

    def _get_lineage(self):
        # Do some processing to get inlets/outlets

        return inlets, outlets
```

If you override the `pre_execute` and `post_execute` function, ensure they include the `@prepare_lineage` and `@apply_lineage` decorators respectively. Reference the [Airflow docs](https://airflow.apache.org/docs/apache-airflow/stable/administration-and-deployment/lineage.html#lineage) for more details.

### Custom Extractors

Note: these are only supported in the v2 plugin.

You can also create a custom extractor to extract lineage from any operator. This is useful if you're using a built-in Airflow operator for which we don't support automatic lineage extraction.

See this [example PR](https://github.com/datahub-project/datahub/pull/10452) which adds a custom extractor for the `BigQueryInsertJobOperator` operator.

## Cleanup obsolete pipelines and tasks from Datahub

There might be a case where the DAGs are removed from the Airflow but the corresponding pipelines and tasks are still there in the Datahub, let's call such pipelines ans tasks, `obsolete pipelines and tasks`

Following are the steps to cleanup them from the datahub:

- create a DAG named `Datahub_Cleanup`, i.e.

```python
from datetime import datetime

from airflow import DAG
from airflow.operators.bash import BashOperator

from datahub_airflow_plugin.entities import Dataset, Urn

with DAG(
    "Datahub_Cleanup",
    start_date=datetime(2024, 1, 1),
    schedule_interval=None,
    catchup=False,
) as dag:
    task = BashOperator(
        task_id="cleanup_obsolete_data",
        dag=dag,
        bash_command="echo 'cleaning up the obsolete data from datahub'",
    )

```

- ingest this DAG, and it will remove all the obsolete pipelines and tasks from the Datahub based on the `cluster` value set in the `airflow.cfg`

## Get all dataJobs associated with a dataFlow

If you are looking to find all tasks (aka DataJobs) that belong to a specific pipeline (aka DataFlow), you can use the following GraphQL query:

```graphql
query {
  dataFlow(urn: "urn:li:dataFlow:(airflow,db_etl,prod)") {
    childJobs: relationships(
      input: { types: ["IsPartOf"], direction: INCOMING, start: 0, count: 100 }
    ) {
      total
      relationships {
        entity {
          ... on DataJob {
            urn
          }
        }
      }
    }
  }
}
```

## Emit Lineage Directly

If you can't use the plugin or annotate inlets/outlets, you can also emit lineage using the `DatahubEmitterOperator`.

Reference [`lineage_emission_dag.py`](../../metadata-ingestion-modules/airflow-plugin/src/datahub_airflow_plugin/example_dags/lineage_emission_dag.py) for a full example.

In order to use this example, you must first configure the Datahub hook. Like in ingestion, we support a Datahub REST hook and a Kafka-based hook. See the plugin configuration for examples.

## Debugging

### Missing lineage

If you're not seeing lineage in DataHub, check the following:

- Validate that the plugin is loaded in Airflow. Go to Admin -> Plugins and check that the DataHub plugin is listed.
- With the v2 plugin, it should also print a log line like `INFO  [datahub_airflow_plugin.datahub_listener] DataHub plugin v2 using DataHubRestEmitter: configured to talk to <datahub_url>` during Airflow startup, and the `airflow plugins` command should list `datahub_plugin` with a listener enabled.
- If using the v2 plugin's automatic lineage, ensure that the `enable_extractors` config is set to true and that automatic lineage is supported for your operator.
- If using manual lineage annotation, ensure that you're using the `datahub_airflow_plugin.entities.Dataset` or `datahub_airflow_plugin.entities.Urn` classes for your inlets and outlets.

### Incorrect URLs

If your URLs aren't being generated correctly (usually they'll start with `http://localhost:8080` instead of the correct hostname), you may need to set the webserver `base_url` config.

```ini title="airflow.cfg"
[webserver]
base_url = http://airflow.mycorp.example.com
```

### TypeError ... missing 3 required positional arguments

If you see errors like the following with the v2 plugin:

```shell
ERROR - on_task_instance_success() missing 3 required positional arguments: 'previous_state', 'task_instance', and 'session'
Traceback (most recent call last):
  File "/home/airflow/.local/lib/python3.8/site-packages/datahub_airflow_plugin/datahub_listener.py", line 124, in wrapper
    f(*args, **kwargs)
TypeError: on_task_instance_success() missing 3 required positional arguments: 'previous_state', 'task_instance', and 'session'
```

The solution is to upgrade `acryl-datahub-airflow-plugin>=0.12.0.4` or upgrade `pluggy>=1.2.0`. See this [PR](https://github.com/datahub-project/datahub/pull/9365) for details.

## Compatibility

We no longer officially support Airflow <2.3. However, you can use older versions of `acryl-datahub-airflow-plugin` with older versions of Airflow.
The first two options support Python 3.7+, and the last option supports Python 3.8+.

- Airflow 1.10.x, use DataHub plugin v1 with acryl-datahub-airflow-plugin <= 0.9.1.0.
- Airflow 2.0.x, use DataHub plugin v1 with acryl-datahub-airflow-plugin <= 0.11.0.1.
- Airflow 2.2.x, use DataHub plugin v2 with acryl-datahub-airflow-plugin <= 0.14.1.5.

DataHub also previously supported an Airflow [lineage backend](https://airflow.apache.org/docs/apache-airflow/2.2.0/lineage.html#lineage-backend) implementation. While the implementation is still in our codebase, it is deprecated and will be removed in a future release.
Note that the lineage backend did not support automatic lineage extraction, did not capture task failures, and did not work in AWS MWAA.
The [documentation for the lineage backend](https://docs-website-1wmaehubl-acryldata.vercel.app/docs/lineage/airflow/#using-datahubs-airflow-lineage-backend-deprecated) has already been archived.

## Additional references

Related Datahub videos:

- [Airflow Lineage](https://www.youtube.com/watch?v=3wiaqhb8UR0)
- [Airflow Run History in DataHub](https://www.youtube.com/watch?v=YpUOqDU5ZYg)